Ahmad Tahmasebi; Farzaneh Aram; Hassan Pakniyat; Ali Niazi; Elahe Tavakol; Esmaeil Ebrahimie
Abstract
Co-expression analysis is a useful tool to analysis data and detection of genes that act in the same pathway or biological process. Echinacea purpurea is one of the most important medicinal plant of the Asteraceae family that is known as antioxidative and antiviral agent. Despite medicinal importance ...
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Co-expression analysis is a useful tool to analysis data and detection of genes that act in the same pathway or biological process. Echinacea purpurea is one of the most important medicinal plant of the Asteraceae family that is known as antioxidative and antiviral agent. Despite medicinal importance of E. purpurea, very few reports are available for metabolic mechanisms in this plant. With the aim to elucidate the gene expression profiling and identification of modules in E. purpurea, we performed a systems biology analysis on publicly available transcriptome data. Gene ontology and KEGG pathway enrichment analysis revealed that the unigenes were highly related to the cellular process, primary metabolic process, carbon metabolism and biosynthesis of antibiotics. The co-expression networks divided genes into multiple modules. Of these, module M2 associated with secondary metabolic process. Moreover, a total of 47 transcription factor families such as bHLH, bZIP, C2H2, MYB and WRKY in modules were identified. These findings can provide an overall picture for better understanding the gene expression patterns and common transcriptional mechanisms in E. purpurea.
Tahereh Deihimi; Esmaeil Ebrahimie; Ali Niazi; Mansour Ebrahimi; Shahab Ayatollahi; Ahmad Tahmasebi; Touraj Rahimi; Moein Jahanbani Veshareh
Abstract
Applying microorganism in oil recovery has attracted attentions recently. Surfactin produced by Bacillus subtilis is widely used industrially in a range of industrial applications in pharmecutical and environmental sectors. Little information about molecular mechanism of suffactin compound is available. ...
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Applying microorganism in oil recovery has attracted attentions recently. Surfactin produced by Bacillus subtilis is widely used industrially in a range of industrial applications in pharmecutical and environmental sectors. Little information about molecular mechanism of suffactin compound is available. In this study, we performed promoter and network analysis of surfactin production genes in Bacillus subtilis subsp. MJ01 (isolated from oil contaminated soil in South of Iran), spizizenii and 168. Our analysis revealed that comQ and comX are the genes with sequence alterations among these three strains of Bacillus subtilis and are involved in surfactin production. Promoter analysis indicated that lrp, argR, rpoD, purr and ihf are overrepresented and have the highest number of transcription factor binding sites (TFBs) on the key surfactin production genes in all 3 strains. Also the pattern of TFBs among these three strains was completely different. Interestingly, there is distinct difference between 168, spizizenii and MJ01 in their frequency of TFs that activate genes involve in surfactin production. Attribute weighting algorithms and decision tree analysis revealed ihf, rpoD and flHCD as the most important TF among surfactin production. Network analysis identified two significant network modules. The first one consists of key genes involved in surfactin production and the second module includes key TFs, involved in regulation of surfactin production. Our findings enhance understanding the molecular mechanism of surfactin production through systems biology analysis.
Nassim Rahmani; Esmaeil Ebrahimie; Ali Niazi; Najaf Allahyari Fard; Bijan Bambai; Zarrin Minuchehr; Mansour Ebrahimi
Abstract
Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering ...
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Allergens are proteins or glycoproteins which make widespread disorders that can lead to a systemic anaphylactic shock and even death within a short period of time. Understanding the protein features that are involved in allergenicity is important in developing future treatments as well as engineering proteins in genetic transformation projects. A big dataset of 1439 protein features from 761 plant allergens and 7815 non-allergen proteins was constructed. Thereafter, 10 different attribute weighting algorithms were utilized to find the key characteristics differentiating allergens and non-allergen proteins. The frequency of Leu, Arg and Gln selected by different attribute weighting algorithms with more than 50% confidence, including attribute weighting by Weight_Info Gain, Weight Chi Squared, Weight_Gini Index and Weight_Relief. High amount of Gln and low percentage of Leu and Arg discriminate plant allergens from non-allergens
Mansour Ebrahimi; Esmaeil Ebrahimie; Narjes Rahpayma
Abstract
We used various screening techniques, clustering, decision tree and generalized rule induction (association) (GRI) models and molecular phylogenic relationship to search for patterns of halophi-licy and to find features contribute to halolysin salt stability. We found Met was the sole N-terminal amino ...
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We used various screening techniques, clustering, decision tree and generalized rule induction (association) (GRI) models and molecular phylogenic relationship to search for patterns of halophi-licy and to find features contribute to halolysin salt stability. We found Met was the sole N-terminal amino acid in halolysin proteins, whereas other amino acids found at this position of oth-er proteases and termitase. Eighty-three protein features were shown to be important in feature selection modeling, and just one peer group with an anomaly index of 2.42 declined to 1.87 after being run using only important selected features. The depth of the trees generated by various de-cision tree models varied from 1 to 5 branches. The number of peer groups in clustering models was reduced significantly (p
